| A biologically motivated mechanism for self-organizing a neural network with modi able lateral connections is presented. The weight modi cation rules are purely activity-dependent, unsupervised and local. The lateral interaction weights are initially random but develop into a \Mexican hat" shape around each neuron. At the same time, the external inputweights self-organize to form a topologica...

An extension of a recently proposed evolutionary selforganizing map is introduced and applied to the tracking of objects in video sequences. In the proposed approach, a geometric template consisting of a small number of keypoints is used to track an object that moves smoothly. The coordinates of the keypoints and their neighborhood relations are associated with the coordinates of the nodes of a...

In this thesis, a study on gene expression data analysis is done using some supervised, unsupervised and semi-supervised approaches. The task of class prediction for six gene expression datasets (namely, Brain Tumor, Colon Cancer, Leukemia, Lymphoma and SRBCT) has been carried out. Here, a one-dimensional self-organizing feature maps (SOFM) in a semi-supervised learning framework is developed f...

Automatic fuzzy membership generation is important in pattern recognition. A new scheme is proposed to generate fuzzy membership functions with unsupervised learning using self-organizing feature map. Simulation results on different datasets support this new scheme. 2005 Elsevier B.V. All rights reserved. PACS: 07.05.Mh

This paper presents a compression scheme for color images, by using Self-Organizing Feature Map algorithm which is a neural network structure. In this application 1-dimensional SOFM is used to map 256-color to 64-, 32and 16-color. After the quantization process, relative coding and entropy coding are performed without any loss in the information.

Kohonen’s se l f -organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes , the quantization and dinlensionality reduct ion of data . A short descr ipt ion of i t s h is tory and i ts b io logical context Ls g iven. We show that the inherent c lass i f icat ion propcrtim of the feature map make it a sui...

Invielen Föllen entspricht die Verteilung von empirisch erhobenen Daten nicht einer Normalverteilung. Um eine vergleichbare Slcalierung der Daten zu effeichen, ist eine Transformation in eine Normalverteilung oder zumindest in eine symmetrische Verteilung notwendig. Desweiteren basieren viele statistische verfahren auf der Annahme einer Normalverteilung. Die Bestimmung einer geeigneten Transfor...

during an 11 days mission in february 2000 the shuttle radar topography mission (srtm) collected data over 80% of the earth's land surface, for all areas between 60 degrees n and 56 degrees s latitude. since srtm data became available, many studies utilized them for application in topography and morphometric landscape analysis. exploiting srtm data for recognition and extraction of topographic ...

The Self-Organizing Map has been successfully applied in numerous industrial applications. An important task in data analysis is finding and visualizing multiple dependencies in data. In this paper, we propose a method for visualizing the Self-Organizing Map by decomposing the feature dimensions into groups with high correlation or selections by domain experts. Using Gradient Visualization we p...